MDP 1.1.0
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http://mdp-toolkit.sourceforge.net/
Modular toolkit for Data Processing (MDP) is a Python library to
perform data processing. Already implemented algorithms include:
Principal Component Analysis (PCA), Independent Component Analysis
(ICA), Slow Feature Analysis (SFA), and Growing Neural Gas (GNG).
MDP allows to combine different algorithms and other data processing
elements (nodes) into data processing sequences (flows). Moreover, it
provides a framework that makes the implementation of new algorithms
easy and intuitive.
MDP supports the most common numerical extensions to Python, currently
Numeric, Numarray, SciPy. When used together with SciPy and the symeig
package, MDP gives to the scientific programmer the full power of
well-known C and FORTRAN data processing libraries. MDP helps the
programmer to exploit Python object oriented design with C and FORTRAN
efficiency.
MDP has been written for research in neuroscience, but it has been
designed to be helpful in any context where trainable data processing
algorithms are used. Its simplicity on the user side together with the
reusability of the implemented nodes could make it also a valid
educational tool.
Requirements:
* Python >= 2.3
* one of the following Python numerical extensions:
Numeric, Numarray, or SciPy.
For optimal performance we recommend to use SciPy with LAPACK
and ATLAS libraries, and to install the symeig module.
Most important changes since version 1.1.0:
- MDP now runs with Numeric or Numarray. Of course we still recommend SciPy
for maximal performance.
- Migration to new-style classes.
- Flow are now container type objects and share many methods with built-in
list objects.
- A comprehensive tutorial has been written, that should introduce the novice
to basic and advanced features of the package.
- Refactoring and cleaning up.
- symeig is now distributed as an independent package (no compilation is
needed to install MDP).
(sorry for multiple posting)
--
Tiziano Zito
Institute for Theoretical Biology
Humboldt-Universitaet zu Berlin
Invalidenstrasse, 43
D-10115 Berlin, Germany
http://itb.biologie.hu-berlin.de/~zito/